Video tracking and recognition technology based on computer vision has been widely used in public security, authentication and other occasions. For instance, suspicious objects can be tracked by continuous video tracking of the suspicious objects in the surveillance video, and the suspicious objects can be further identified through intelligent recognition of the suspicious objects so as to provide powerful clues for the criminal investigation process.
However, it is hard for the existing video tracking method to complete the continuous tracking of the same tracking object, since the sampling frame rate of the surveillance video is not high and the camera may violently move during the sampling process. The existing video tracking method cannot determine whether the current tracking object is the same as the previous tracking object when a breakpoint occurs in the tracking process, and the discontinuity of the tracking process may cause the loss of the tracking object. In addition, the existing object recognition method based on video tracking is also complex, and the recognition efficiency is low.